Data-backed research on tech hiring. All numbers come from the LeakCode database.
We pulled every Amazon question in the LeakCode database and ran the numbers. 4,577 clean entries across 7 sources. Here is what the data shows about Amazon's coding rounds, OAs, and system design in 2026.
Read more →There is a forum with thousands of real FAANG interview reports that most English-speaking candidates have never heard of. LeakCode pulls from it. Here is what it is, why it matters, and what the 3,553 questions we have indexed from it actually show.
Read more →LeakCode analyzed 33,014 real candidate-reported questions to surface what FAANG companies actually ask in 2026. Top companies by volume, round type distribution, and source breakdown — all real numbers from the database.
Read more →LeakCode has tagged 1,722 system design rounds across 33,000+ candidate reports. Five thematic clusters — storage, scale, caching, queuing, and real-time — account for the majority of what companies actually ask. Here is the breakdown.
Read more →Most interview prep treats 'senior engineer' as a single target. LeakCode's aggregated Google reports show that L4 and L5 loops have meaningfully different calibration signals in coding speed, system design scope, and behavioral weighting.
Read more →Behavioral prep advice usually treats all FAANG companies as interchangeable. LeakCode's aggregated reports show they are not. Here is how Amazon, Google, Meta, and Microsoft each run behavioral rounds differently.
Read more →A static question list from 2023 is not the same as a live question database updated from 2026 reports. LeakCode tracks question rotation across FAANG. Here is what the data shows about how fast company pools change.
Read more →LeakCode analyzed 6,003 phone screen and 1,347 onsite-tagged reports. The difficulty gap is real but the nature of that gap — harder problems vs higher simultaneous demands — is widely misunderstood.
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